The Challenge

Most organizations approach AI adoption reactively. Someone sees a competitor using chatbots, another team experiments with document automation, leadership asks about "what we're doing with AI." The result is fragmented pilots that don't connect to business strategy.

Without systematic evaluation, teams chase shiny tools instead of solving real problems. Resources scatter across low-impact experiments while high-value opportunities sit untouched because no one mapped them against organizational priorities.

The Approach

Portfolio thinking treats AI opportunities like investment decisions. Each potential use case gets evaluated against consistent criteria: strategic alignment, implementation complexity, resource requirements, and expected returns.

This creates a ranked pipeline where leadership can see exactly which initiatives deserve funding, which need more validation, and which should wait. Teams stop competing for attention and start collaborating on shared priorities.

Core Principles

Four principles guide effective AI use case portfolio management:

  • Strategic Alignment FirstEvery AI initiative must connect to documented business objectives. If you can't draw a clear line from the use case to strategic priorities, it doesn't belong in the portfolio regardless of how interesting the technology seems.
  • Consistent Evaluation CriteriaAll opportunities get scored against the same dimensions. This prevents the loudest advocate from winning and ensures quiet but valuable opportunities surface alongside flashy proposals.
  • Implementation Reality CheckTechnical feasibility matters, but so does organizational readiness. A brilliant AI solution that requires data your systems don't capture or skills your team lacks isn't ready for the active portfolio.
  • Portfolio BalanceMix quick wins with longer-term transformations. Organizations that only pursue safe, incremental AI miss breakthrough opportunities. Those that only chase moonshots never build momentum.

Application Example

Regional Accounting Firm: From 47 Ideas to 5 Priorities

Challenge: Partners across eight practice areas had submitted AI project requests ranging from automated tax research to client communication bots. No framework existed to compare a $15K experiment against a $200K platform investment. Leadership was paralyzed by options.
Application: Portfolio evaluation surfaced that 31 requests were variations of three core capabilities. Scoring revealed the highest-impact opportunity wasn't on anyone's original list. It emerged from combining elements across proposals. The firm launched with five coordinated initiatives instead of 47 scattered experiments.

Implementation Scope

Timeline varies based on organization size and number of potential use cases to evaluate:

2-4

Assessment Phase

Weeks to inventory existing AI initiatives and capture new opportunities across the organization

4-8

Implementation

Weeks to score, rank, and build the prioritized portfolio with resource allocation

Ongoing

Optimization

Quarterly reviews to update rankings as initiatives progress and new opportunities emerge